Discrimination between different degrees of coronary artery disease using time-domain features of the finger photoplethysmogram in response to reactive hyperemia

Hosseini, Zahra Sadat; Zahedi, Edmond; Attar, Hamid Movahedian; Fakhrzadeh, Hossein; Parsafar, Mohammad Habib

doi:10.1016/j.bspc.2014.12.011

Cited by 19 publications

(5 citation statements)

References 48 publications

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“…A PPG signal contains information on the heart’s systolic and diastolic activity, hemodynamics, and hemorheology. Therefore, in a PPG signal and its derivatives, many potential physiological characteristics [12,33] can be defined or quantified. Some of these characteristics have been found and confirmed by a large number of researchers; for example, the characteristics of the systolic and diastolic periods, crest time, augmentation index, and large artery stiffness index [33].…”

Section: Methodsmentioning

confidence: 99%

“…Photoplethysmography (PPG) provides abundant physiological information [12] about the operation of the cardiovascular circulation system at a low cost and with convenient signal acquisition; therefore, it is of great interest to researchers. A series of in-depth investigations [13,14,15,16,17] have been carried out to non-invasively assess BP, blood sugar, stiffness, and fatigue levels using PPG signals.…”

Section: Introductionmentioning

confidence: 99%

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Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach

Liang

Chen

Ward

et al. 2018

JCM

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Hypertension is a common chronic cardiovascular disease (CVD). Early screening and diagnosis of hypertension plays a major role in its prevention and in the control of CVDs. Our study discusses the early screening of hypertension while using the morphological features of photoplethysmography (PPG). Numerous morphological features of PPG and its derivative waves were defined and extracted. Six types of feature selection methods were chosen to screen and evaluate these PPG morphological features. The optimal features were comprehensively analyzed in relation to the physiological processes of the cardiovascular circulatory system. Particularly, the intrinsic relation and physiological significance between the formation process of systolic blood pressure (SBP) and PPG morphology features were analyzed in depth. A variety of linear and nonlinear classification models were established for the comparison trials. The F1 scores for the normotension versus prehypertension, normotension and prehypertension versus hypertension, and normotension versus hypertension trials were 72.97%, 81.82%, and 92.31%, respectively. In summary, this study established a PPG characteristic analysis model and established the intrinsic relationship between SBP and PPG characteristics. Finally, the risk stratification of hypertension at different stages was examined and compared based on the optimal feature subset.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach

Liang

Chen

Ward

et al. 2018

JCM

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“…The input signal was smoothed to reduce the dimensionality, and the smoothing accuracy was used to explore the features in the multilayer feed-forward networks that were highly parallelized (MFN), and this achieved a classification rate for testing datasets of 94.7% and training datasets of 100%. Hosseini et al [ 48 ] utilized finger PPG, a noninvasive optical signal collected before and after reactive hyperemia, to distinguish between people with various CVDs, with a maximum accuracy of 81.5% for the KNN classifier. Shobitha et al [ 49 ] used the extreme learning machine (ELM), a supervised learning algorithm, to classify PPG signals as normal or affected by cardiovascular illness and compared its performance with backpropagation and support vector machine (SVM) techniques.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals

Palanisamy,

Rajaguru

2023

Bioengineering

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Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection of cardiovascular disease (CVD) from PPG signals. PPG signals occupy a large amount of memory and, hence, the signals were dimensionally reduced in the initial stage. A total of 41 subjects from the Capno database were analyzed in this study, including 20 CVD cases and 21 normal subjects. PPG signals are sampled at 200 samples per second. Therefore, 144,000 samples per patient are available. Now, a one-second-long PPG signal is considered a segment. There are 720 PPG segments per patient. For a total of 41 subjects, 29,520 segments of PPG signals are analyzed in this study. Five dimensionality reduction techniques, such as heuristic- (ABC-PSO, cuckoo clusters, and dragonfly clusters) and transformation-based techniques (Hilbert transform and nonlinear regression) were used in this research. Twelve different classifiers, such as PCA, EM, logistic regression, GMM, BLDC, firefly clusters, harmonic search, detrend fluctuation analysis, PAC Bayesian learning, KNN-PAC Bayesian, softmax discriminant classifier, and detrend with SDC were utilized to detect CVD from dimensionally reduced PPG signals. The performance of the classifiers was assessed based on their metrics, such as accuracy, performance index, error rate, and a good detection rate. The Hilbert transform techniques with the harmonic search classifier outperformed all other classifiers, with an accuracy of 98.31% and a good detection rate of 96.55%.

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“…Photoplethysmography (PPG) is a non-invasive optical measurement technique for measuring blood volume changes with each pulse. PPG has been widely used to predict vital health-related parameters based on unique features extracted from PPG signal and its derivative waveforms (Hosseini et al 2015, Suganthi et al 2015. For example, PPG signals can be used to monitor different aspects of cardiovascular conditions, such as heart rate, blood velocity, hypertension, heart rate variability, and augmentation index, as well as large artery stiffness index (SI) (Akselrod et al 1981, Oliver and Webb 2003, Suganthi et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Modified photoplethysmography signal processing and analysis procedure for obtaining reliable stiffness index reflecting arteriosclerosis severity

Wu¹,

Liu²,

Tzeng³

et al. 2022

Physiol. Meas.

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Objective. This study aimed to describe a modified photoplethysmography (PPG) signal processing and analysis procedure to obtain a more reliable arterial stiffness index (SI). Approach. Three parameters were used to assess the PPG signal quality without prominent diastolic waves, which are similar to a sinusoidal waveform shape. The first parameter, sinusoidal ratio (S-value), was based on frequency-domain analysis: a higher S-value indicated the presence of PPG pulse wave with unapparent diastolic peak. The second parameter was the time difference between systolic peak-to-diastolic peak and the systolic peak-to-dicrotic notch. The third parameter was the percentage of sin-like waveform in the PPG signals. The applicability of these parameters was demonstrated in 40 participants, including 11 with apparent diastolic peaks in the PPG signals and 29 with unapparent diastolic peaks. Main results. An S-value of >3.5 indicated apparent diastolic peaks in the PPG signals. In addition, a systolic peak-to-diastolic peak time difference >80% and a sin-like waveform >55% may be associated with severity of vascular aging. Significance. These parameters successfully detected low-quality PPG signals with unapparent diastolic waveform before SI calculation, thereby ensuring the accuracy of subsequent evaluation of cardiovascular-related disease and clinical risk stratification.

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Discrimination between different degrees of coronary artery disease using time-domain features of the finger photoplethysmogram in response to reactive hyperemia

Cited by 19 publications

References 48 publications

Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach

Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals

Modified photoplethysmography signal processing and analysis procedure for obtaining reliable stiffness index reflecting arteriosclerosis severity

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